Single-photon Image Super-resolution via Self-supervised Learning

• URL: http://arxiv.org/abs/2303.02033v1
• Date: Fri, 3 Mar 2023 15:52:01 GMT
• Title: Single-photon Image Super-resolution via Self-supervised Learning
• Authors: Yiwei Chen, Chen Jiang and Yu Pan
• Abstract summary: Single-Photon Image Super-Resolution (SPISR) aims to recover a high-resolution photon counting cube from a noisy low-resolution one by computational imaging algorithms. By extending Equivariant Imaging (EI) to single-photon data, we propose a self-supervised learning framework for the SPISR task.
• Score: 6.218646347012887
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Single-Photon Image Super-Resolution (SPISR) aims to recover a high-resolution volumetric photon counting cube from a noisy low-resolution one by computational imaging algorithms. In real-world scenarios, pairs of training samples are often expensive or impossible to obtain. By extending Equivariant Imaging (EI) to volumetric single-photon data, we propose a self-supervised learning framework for the SPISR task. Particularly, using the Poisson unbiased Kullback-Leibler risk estimator and equivariance, our method is able to learn from noisy measurements without ground truths. Comprehensive experiments on simulated and real-world dataset demonstrate that the proposed method achieves comparable performance with supervised learning and outperforms interpolation-based methods.

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